EXAMINING THE EFFECTS OF THE CUSTOMER LOYALTY STATES ON THE WORD OF MOUTH

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1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2009 Proceedings Pacific Asia Conference on Information Systems (PACIS) July 2009 EXAMINING THE EFFECTS OF THE CUSTOMER LOYALTY STATES ON THE WORD OF MOUTH Sanjit Kumar Roy ICFAI Business School, Hyderabad; ICFAI University, Gul Butaney Bentley University, Boston, Bhupin Bhutaney Adelphi University, Long Island, New York, Follow this and additional works at: Recommended Citation Roy, Sanjit Kumar; Butaney, Gul; and Bhutaney, Bhupin, "EXAMINING THE EFFECTS OF THE CUSTOMER LOYALTY STATES ON THE WORD OF MOUTH" (2009). PACIS 2009 Proceedings This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2009 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 EXAMINING THE EFFECTS OF THE CUSTOMER LOYALTY STATES ON THE WORD OF MOUTH ABOUT RETAIL WEB-SITES Abstract This study empirically examines the Influence of the Cognitive, Affective, Conative and Action states as conceptualized by Oliver (1999) on the Word-of-mouth behavior about retail websites from the customers of the sites. Structural equation model is used to estimate the path coefficients between the loyalty states and the Word- of- Mouth- behavior. Data collected from 511 under graduate and graduate students from a business university suggest the existence of significant paths from the Affective, Conative and Action loyalty states to site-customer s word of mouth about the website. Results of the study, managerial implications and directions for future research are discussed. Keywords: Cognitive, Affective, Conative, Action loyalty, Word-of-mouth

3 1. INTRODUCTION One of the most widely accepted notions in consumer behavior is that word-of-mouth (WOM) communication plays an important role in shaping the target-consumers attitudes and behaviors. This is because the WOM is regarded as providing more reliable, trustworthy advice, and personal contacts are generally able to offer social support and encouragement (Arndt 1967; Day 1972). It plays an important role in shaping consumer attitudes and behaviors towards products and firms (Brown and Reingen, 1987). Katz and Lazarsfeld s study, back in 1955, found that word-of-mouth was four to seven times more effective than advertising in newspaper or magazine media and personal selling promotion tool, in influencing consumers to switch brands. Similarly, Day (1971) found that word-of-mouth was nine times as effective as traditional media advertising, in converting consumer unfavorable or neutral predisposition into positive attitudes. Creating and reinforcing favorable attitudes largely rests with the ability of the brand to generate favorable word of mouth. Mazzarol, Sweeny and Startar (2007) suggest that the word-of-mouth is a new way to achieve competitive advantage in the market place. It can be viewed as an indicator of customer loyalty. Srinivasan et al. (2002) posits that one of the behavioral outcomes of e-loyalty is positive word of mouth. Hagel and Armstrong (1997) and Dick and Basu (1994), state that loyal customers are more likely to engage in positive word of mouth. Loyal customers are reported to have higher retention rates, commit a higher share of their spending and are more likely to recommend others to become customers of the firm (Zeithmal, 2000). Building and maintaining customer loyalty, therefore has been a central theme of marketing theory and practice in achieving sustainable competitive advantage (Gommans et al., 2001). Scholars have made advances in delineating the loyalty states and stages through which customers become loyal to firms and products (Jacoby and Chestnut, 1978; Dick and Basu, 1994; Oliver 1997; Uncles, Dowling and

4 Hammond, 2003; Harris and Goode, 2004). For example, Oliver s (1997) four stage loyalty chain model suggests that customers go through four distinct and sequential stages. They first develop cognitive loyalty, which is followed by the cultivation of affective loyalty, conative loyalty, and finally action loyalty. A review of literature suggests that consumer attitude is the key construct to understand the customer loyalty behavior (e.g., patronage for the firm, WOM). With recent interest in the loyalty states (e.g., Harris and Goodie, 2004; Gentry and Kalliny 2008), this paper aims to examine how each loyalty state influences the WOM in the context of retail Web sites. However, the WOM behavior is not yet been fully looked at from the perspective of loyalty states. Majority of the research in the past has primarily focused on identifying the drivers of customer loyalty, e.g., the customer satisfaction, trust, switching costs, etc (e.g., Methlie and Nysveen, 1999). The current study expands the research stream from a nomological perspective, by examining the relationships between the four loyalty states and the WOM. In this study, we propose and empirically test an integrated model of antecedents, the four loyalty states, to positive consumer word-of-mouth. For this study, we use the word-of-mouth behavior which may be on/off line. It refers to the sender of WOM talking positive experiences about the website, recommending the website, and/or showing the website to others. With the advent and growth of B2C (Business to Customer) e-commerce, the importance of building a loyal visitor base to an e-commerce website has increased. Today s consumers are perhaps the most connected and informed in history; sharing with all their opinions on products, brands, and websites. Given this rising trend in consumer behavior towards the word-of-mouth sharing of information, it is insightful to find out how the level of WOM behavior varies among the four loyalty states. Many

5 scholars see the company s ability to cultivate positive word-of-mouth from their customers and visitors to their sites, as a strategic weapon. 2. HYPOTHESES AND THE RESEARCH MODEL The conceptual framework of this study is shown in figure 1. The framework presents the relationship between the four stages of loyalty with word of mouth. The core constructs in the model are briefly conceptualized as follows: 2.1 Word-of-mouth about web-site Sun et al. (2006) defined online word-of-mouth as personal experiences and opinions transmitted through written words. Phelps et al. (2004) claim that online WOM is more influential than the traditional WOM because of its certain unique features such as convenience, speed, one-to-many, many-to-many and no face-to-face interaction. In this study online word-of-mouth is defined as the extent to which customers share positive experiences with the website and recommend others to switch to or use the particular website. 2.2 Cognitive loyalty According to Oliver (1999) and Dick and Basu (1994) cognitive loyalty refers to the loyalty state based on brand beliefs. They state that loyalty at this phase is directed at the brand because of the attribute performance levels. Based on this we conceptualize cognitive loyalty as customers beliefs about the quality of the website features. The more positive is the site customer about the quality of various attributes of website, the higher the level of the customer s cognitive loyalty. 2.3 Affective loyalty Affective loyalty indicates the level of favorable attitudes and liking that the customer displays towards the site (or brand). at this phase is directed at the degree of affect for the brand (Oliver

6 1999; Dick and Basu, 1994). We conceptualize affective loyalty as the extent to which customers likes and/or enjoys their experience on the website. The more the customer likes and perceives the website as providing fulfilling experience, higher would be the customer s affective loyalty. 2.4 Conative loyalty The authors define conative loyalty as the development of behavioral intention to continue to buy the brand or the site. This loyalty state is characterized by a deeper level of commitment (Hennig-Thurau et al. 2002; Janda et al. 2002). We conceptualize conative loyalty as the extent to which the customer displays commitment to continued usage of the website in future. It is accompanied by a willingness to overcome any impediments to pursue such intentions, e.g., continuing to make purchases from the site even though the competing web sites may offer better prices or values. 2.4 Action (Behavioral) loyalty Oliver (1999) states that action or behavioral loyalty is the stage where behavioral intentions get converted into actions. We conceptualize action loyalty as the extent to which customers uses and visits the website, spends time and money to make purchases relative to other websites. Cognitive Affective Conative Action Online WOM Figure 1. The Research Framework

7 Oliver (1997) states that brand loyalty stages reveal a learning process which highlights the relationship between attitude and behavior. He adds that attitudinal loyalty should be viewed as a three component structure and developing in three phases, viz. cognitive, affective and conative components of attitudinal loyalty. This has support in the attitude literature (Breckler, 1984). Further Oliver (1997, p.392) claims that the attitudinal brand loyalty should be considered as a sequential process in which the customers become loyal first in cognitive sense, then later in affective sense and still later in conative manner. Based on Ajzen and Fishbein (1980) theory of planned behavior the three stages of attitudinal loyalty should be linked to behavioral/action loyalty. Based on the above argument and Harris and Goode s (2004) empirical work we propose the following hypotheses in case of retail websites: H1: Cognitive loyalty has a positive and direct impact on affective loyalty. H2: Affective loyalty has a positive and direct impact on conative loyalty. H3: Conative loyalty has a positive and direct impact on action loyalty. Gremler (1995) and Dick and Basu (1994) examined the behavioral outcomes of customer loyalty on consumer behavior. Srinivasan et al. (2002) posit that one of the behavioral outcomes of e-loyalty is positive word of mouth. Hagel and Armstrong (1997) and Dick and Basu (1994), state that loyal customers are more likely to involve in positive word of mouth. The positive and direct relationship between customer loyalty and customer s willingness to recommend further finds support in Reichheld (2003, 2006). Here we propose that all the four stages of loyalty will have a positive and direct relationship with online word of mouth. Hence, we propose that (i) the higher the level of cognitive loyalty, it is reasonable to expect that it will produce relatively higher level of the customer s WOM about site; (ii) the higher the level of affective loyalty, higher would be the customer s WOM about site; (iii) the higher the level of conative intentions, higher would be the customer s WOM about site and (iv) the higher the level of action or behavioral loyalty, higher would be the customer s WOM about the site.

8 Hence, the following hypotheses: H4: Cognitive loyalty has a positive and direct impact on online word of mouth. H5: Affective loyalty has a positive and direct impact on online word of mouth. H6: Conative loyalty has a positive and direct impact on online word of mouth. H7: Action loyalty has a positive and direct impact on online word of mouth. 3. RESEARCH METHODOLOGY Data for the study was collected through an online questionnaire survey among the graduate and undergraduate students with business major of a reputed school in Boston. We pursued an elaborate process of generating and adapting items to develop multi-item scales for the research constructs, including the cognitive, affective, conative, and action loyalty components. The measurement items for the WOM construct has been adopted from the literature (Sun et al. 2006; Srinivasan et al. 2002; Gruen et al. 2006). In all 660 questionnaires were distributed out of which 527 were received and of which 511 were usable. So the response rate was 77.42%. The sample included 302 females (59.09%) and 209 males (40.09%). Respondents selected the website that they had most recently visited and with which they had some purchasing experience in the recent past. Data was collected for both goods and services websites. The measurement items are shown in appendix-a. 3.1 Data Analysis and Results Data was analyzed using Anderson and Gerbing s (1988) two step approach whereby the estimation of the confirmatory measurement model precedes the simultaneous estimation of the structural model. A confirmatory factor analysis was conducted on a measurement model involving all the constructs of figure 1, using AMOS Reliability of the constructs was assessed by calculating the Cronbach s alpha and for all the constructs the value was greater than 0.7 which is acceptable (Hair et al., 2006). The

9 composite reliability values for all the constructs were greater than 0.6 which is acceptable for the constructs to be reliable (Bagozzi and Yi, 1988). The output showed that each indicator (factor loadings ranging between and 0.958) loaded significantly on the constructs (p < 0.001), which reflects the convergent validity of the constructs (Anderson and Gerbing, 1988). Further the average variance extracted (AVE) for all the constructs in the model were greater than 0.5 which further supports the convergent validity. The AVE values were greater than the inter-construct squared correlation estimates which supports the discriminant validity of the constructs (Fornell and Larcker, 1981). The model fit showed that (χ 2 = ; df = 179; p<0.001; CFI = 0.934; GFI = 0.920; TLI = 0.922; IFI = 0.934; RFI = 0.901; NFI = 0.916; RMSEA = The values of the fit indices mentioned above indicate a reasonable fit of the measurement model with data (Byrne 2001; p ). Next an SEM was conducted on the structural model using AMOS 7.0 to test the hypotheses formulated in the preceding section. The regression weights of the output and result of the hypotheses testing is shown in table-1. The model fit indices (χ 2 = ; df = 182; p<0.001; CFI = 0.926; GFI = 0.91; TLI = 0.920; IFI = 0.931; RFI 0.896; NFI = 0.910; RMSEA = 0.08 which provided a reasonable model fit for the structural model. Hence, we conclude that the proposed research model fits the data reasonably well. Hypothesized Paths Path Coefficients Hypotheses Affective_ <--- Cognitive_ 0.593** (H1) Accepted Conative_ <--- Affective_ 0.806** (H2) Accepted Action_ <--- Conative_ 0.684** (H3) Accepted Online_WOM <--- Action_ 0.206** (H7) Accepted Online_WOM <--- Affective_ 0.287** (H5) Accepted Online_WOM <--- Conative_ 0.267* (H6) Accepted Online_WOM <--- Cognitive_ (H4) Rejected Note: **Significant at p<0.001; *Significant at p<0.01 Table 1. Standardized Regression Estimates

10 4. DISCUSSION The purpose of this paper was to examine the relationships between the retail website customer s WOM behavior and each of the four loyalty states as conceptualized by Oliver (1999), in the context of retail websites. We found significant path coefficients from Affective, Conative and Action loyalty states to the site customer s WOM behavior about the site, the size of the coefficients being , , and , respectively. As indicated by the path coefficients, both the conative and affective loyalty states relatively seem to prompt the WOM behavior more so than the Action loyalty state. The path between the cognitive loyalty and WOM was not significant, suggesting that the cognitive loyalty base is not very potent in terms of prompting the site customer to spread the positive WOM about the web-site. These results can be rationalized. For example, higher levels of both action loyalty and affective loyalty, compared to the cognitive loyalty state would suggest relatively higher level of customer s satisfaction and fulfilling experience on the site. Since the customer s perceived risk in transacting business on the website is likely to be lower, as judged by the higher level of customer satisfaction and fulfillment of needs and expectations level, the sender of the WOM would feel comfortable in advocating the website to others. Furthermore, over a relatively longer period of satisfactory experience and/or need fulfillment, the customer s conative loyalty state is strengthened. The sender s cognitive dissonance in recommending the website to others is likely to be relatively lower. Lastly, the lack of direct relationship between the cognitive loyalty state and the WOM seems a bit surprising. However, Oliver (1997) argues that the cognitive loyalty is a weaker loyalty state, since this type of commitment at this stage is actually to costs and benefits (e.g. related to performing a task), and not actually a loyalty to the product or service per se. In the context of cognitive loyalty for the website, the costs and benefits would relate to the time spent by the e-customer to accomplish various tasks as well as the extent to which the customer was able to accomplish his or her goals/purposes for visiting the website, free of any hassle or

11 frustration. Cognitive attitude/loyalty provide for functional initiation of purposeful actions. However, it is the customer satisfaction on more substantive aspects of transacting business on the site that would seem to build up the customer s affective and action loyalty states, as well as conative attitudes. 4.1 Managerial Implications and Directions for Future Research To fully understand the nature of the loyalty states and their impact on the WOM behavior about the web-site, an examination of the non-recursive sequential nature of loyalty states and the WOM seem desirable. We do find some theoretical support for extending research in this direction (Fishbein and Ajzen 1975; Triandis 1977; and Baggozi (1982). Also the non-linear relationship between the loyalty states and WOM may be examined in future research. For on-line marketing practitioners, a systematic approach is also essential to understand better the vulnerabilities and sustainers (Oliver, 1997) that would weaken or strengthen the e- customer s loyalty states. Insights gained from this stream of research then would be helpful in developing or identifying integrated e-marketing strategies need to be pursued to strengthen the WOM behavior from the site customers as well as visitors. Specifically, this would call for identifying antecedents unique to each of the three loyalty components, viz., the affective, conative, and action loyalty. References Ajzen, I., & Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: Prentice Hall. Anderson, J. C. and Gerbing, D. W. (1988), Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach, Psychological Bulletin, 103:

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14 Appendix-A: Measurement Instrument Constructs Cognitive Affective Conative Measurement Variables I believe that the overall information quality on this website is very good. I believe the overall service quality offered on this website is very good. I believe the overall navigation quality of this website is very good. I believe this website provides a secure and safe environment for transaction. I like this website much more than other comparable websites. Using this website is very interesting. Using this website is very important to me. Using this website is very exciting. Using this website gives me pleasurable experience. I will continue to buy from this website even though I find better prices on competitors websites. I intend to remain a customer of this website rather than looking for a new website. I intend to expand my use of this website. Action Online WOM I purchase more from this website than other comparable websites. I spend more time on this website than other comparable websites. I visit this website more frequently than other comparable websites. I spend more money on this website than on other comparable websites. I often talk my positive experiences about this website to friends/colleagues. I often recommend this website to others. I often talked to others about the benefits of switching to this website. I have shown this website to others.